Service Provider Positioning Using Forecasted Provisioning Levels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Network computer systems face inefficiencies in resource utilization due to decentralized service provision, leading to increased wait times and burden on computing resources.
Innovation Solution
A network computer system utilizes a provisioning level determination component to forecast and adjust service provider distribution based on historical data and real-time location information, optimizing service provider positioning to meet demand and preferences, thereby reducing wait times and resource burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If service providers are decentralized or distributed to provide services, then service coverage and accessibility are improved, but resource utilization efficiency deteriorates and wait times increase
Solution Approach 1:
The system performs preliminary positioning of service providers to high-provisioning subregions before service requests arrive. The provisioning level determination component forecasts future provisioning levels and proactively positions providers in advance, reducing wait times and improving resource utilization without sacrificing service coverage.
Solution Approach 2:
The system applies different positioning strategies to different subregions based on their specific provisioning levels and service characteristics. Providers are positioned in high-provisioning subregions where they can serve multiple requests efficiently, while maintaining adequate coverage in other areas through targeted positioning operations.
2Loss of time
If more service providers are positioned in high-demand subregions, then service response time is improved, but provider preference and satisfaction deteriorate
Solution Approach 1:
The system changes the parameter of provider positioning by introducing location bias weights that adjust matching probabilities. Providers are positioned in high-provisioning subregions with adjusted matching parameters that account for provider preferences, allowing flexible adjustment between response time and provider satisfaction based on current system state.
Solution Approach 2:
The positioning operations are dynamic and adaptive, adjusting provider positions based on real-time provisioning levels and provider preferences. The system continuously monitors and repositions providers to maintain optimal balance between response time and provider satisfaction, rather than using static positioning.
3Device complexity
If traditional matching systems only consider provider residence location, then matching simplicity is maintained, but service efficiency and wait time reduction are limited
Solution Approach 1:
The system segments the service region into multiple subregions and calculates provisioning levels for each segment independently. This segmentation allows the complex positioning logic to be applied in a structured way, managing complexity through modular subregion analysis while achieving improved service efficiency.
Solution Approach 2:
The provisioning level determination component acts as an intermediary between provider residence locations and service requests. It calculates intermediate provisioning metrics and uses these to guide positioning operations, adding complexity only where needed to improve efficiency while maintaining simple matching for straightforward cases.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A network computer system operates to estimate a quantity of service vehicles operating in a geographic region during a future time interval. For the future time interval, the network computer system determines a current forecast of a provisioning level for a service provided by the projected quantity of service vehicles in each of multiple subregions of the geographic region. The network computer system also determines a location bias for one or more service providers, each operating a corresponding vehicle within the given geographic region. Additionally, the network computer system matches each service provider to a service request based on (i) the location bias of the service provider, (ii) a service location of the service request, and (iii) a determination as to an effect of matching the service provider on the current forecast for the provisioning level.